forked from OSSInnovation/mindspore
62 lines
3.0 KiB
Python
62 lines
3.0 KiB
Python
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# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""
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######################## train lenet example ########################
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train lenet and get network model files(.ckpt) :
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python train.py --data_path /YourDataPath
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"""
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import os
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import argparse
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import mindspore.nn as nn
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from mindspore import context
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from mindspore.train.callback import ModelCheckpoint, CheckpointConfig, LossMonitor, TimeMonitor
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from mindspore.train import Model
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from mindspore.nn.metrics import Accuracy
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from src.dataset import create_dataset
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from src.config import mnist_cfg as cfg
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from src.lenet_fusion import LeNet5 as LeNet5Fusion
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parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
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parser.add_argument('--device_target', type=str, default="Ascend",
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choices=['Ascend', 'GPU', 'CPU'],
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help='device where the code will be implemented (default: Ascend)')
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parser.add_argument('--data_path', type=str, default="./MNIST_Data",
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help='path where the dataset is saved')
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parser.add_argument('--ckpt_path', type=str, default="",
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help='if mode is test, must provide path where the trained ckpt file')
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parser.add_argument('--dataset_sink_mode', type=bool, default=True,
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help='dataset_sink_mode is False or True')
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args = parser.parse_args()
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if __name__ == "__main__":
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context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
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ds_train = create_dataset(os.path.join(args.data_path, "train"), cfg.batch_size, cfg.epoch_size)
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step_size = ds_train.get_dataset_size()
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network = LeNet5Fusion(cfg.num_classes)
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net_loss = nn.SoftmaxCrossEntropyWithLogits(is_grad=False, sparse=True, reduction="mean")
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net_opt = nn.Momentum(network.trainable_params(), cfg.lr, cfg.momentum)
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time_cb = TimeMonitor(data_size=ds_train.get_dataset_size())
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config_ck = CheckpointConfig(save_checkpoint_steps=cfg.epoch_size * step_size,
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keep_checkpoint_max=cfg.keep_checkpoint_max)
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ckpoint_cb = ModelCheckpoint(prefix="checkpoint_lenet", config=config_ck)
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model = Model(network, net_loss, net_opt, metrics={"Accuracy": Accuracy()})
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print("============== Starting Training ==============")
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model.train(cfg['epoch_size'], ds_train, callbacks=[time_cb, ckpoint_cb, LossMonitor()],
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dataset_sink_mode=args.dataset_sink_mode)
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print("============== End Training ==============")
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